Gene expression and mutation-guided synthetic lethality eradicates proliferating and quiescent leukemia cells
Bibliographic record
Abstract
Quiescent and proliferating leukemia cells accumulate highly lethal DNA double-strand breaks that are repaired by 2 major mechanisms: BRCA-dependent homologous recombination and DNA-dependent protein kinase-mediated (DNA-PK-mediated) nonhomologous end-joining, whereas DNA repair pathways mediated by poly(ADP)ribose polymerase 1 (PARP1) serve as backups. Here we have designed a personalized medicine approach called gene expression and mutation analysis (GEMA) to identify BRCA- and DNA-PK-deficient leukemias either directly, using reverse transcription-quantitative PCR, microarrays, and flow cytometry, or indirectly, by the presence of oncogenes such as BCR-ABL1. DNA-PK-deficient quiescent leukemia cells and BRCA/DNA-PK-deficient proliferating leukemia cells were sensitive to PARP1 inhibitors that were administered alone or in combination with current antileukemic drugs. In conclusion, GEMA-guided targeting of PARP1 resulted in dual cellular synthetic lethality in quiescent and proliferating immature leukemia cells, and is thus a potential approach to eradicate leukemia stem and progenitor cells that are responsible for initiation and manifestation of the disease. Further, an analysis of The Cancer Genome Atlas database indicated that this personalized medicine approach could also be applied to treat numerous solid tumors from individual patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".